Efficient hopid based routing for sparse ad hoc networks
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Efficient HopID Based Routing for Sparse Ad Hoc Networks. Yan Chen Lab for Internet & Security Technology (LIST) Northwestern University http://list.cs.northwestern.edu. Outline. Motivation Hop ID and Distance Function Dealing with Dead Ends Evaluation Conclusion.

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Efficient hopid based routing for sparse ad hoc networks

Efficient HopID Based Routing for Sparse Ad Hoc Networks

Yan Chen

Lab for Internet & Security Technology (LIST)

Northwestern University

http://list.cs.northwestern.edu


Outline
Outline

  • Motivation

  • Hop ID and Distance Function

  • Dealing with Dead Ends

  • Evaluation

  • Conclusion


Motivation routing in ad hoc networks
Motivation: Routing in Ad Hoc Networks

  • On-demand routing

    • Flood routing requests

    • No preprocessing needed

    • But poor scalability

  • Geographical routing

    • Use node’s location (or virtual coordinates) as address

    • Greedy routing based on geographic distance


Dead end problem
Dead End Problem

  • Geographic distance dgfails to reflect hop distance dh (shortest path length)

But


Existing work insufficient for sparse ad hoc networks

Connectivity

Greedy success

Existing Work Insufficient for Sparse Ad Hoc Networks

9

1

Geographic routing suffers from dead end problem in sparse networks

worse

0.9

8

GFG/GPSR

0.8

7

0.7

6

0.6

Frequency

5

0.5

Shortest Path Span

0.4

4

GOAFR+

0.3

3

0.2

better

2

0.1

critical

1

0

0

2

4

6

8

10

12

Network Density [nodes per unit disk]

Fabian Kun, Roger Wattenhofer and Aaron Zollinger, Mobihoc 2003


Outline1
Outline

  • Motivation

  • Hop ID and Distance Function

  • Dealing with Dead Ends

  • Evaluation

  • Conclusion


Virtual coordinates
Virtual Coordinates

  • Problem definition

    • Define and build the virtual coordinates, and

    • Define the distance function based on the virtual coordinates

    • Goal: routing based on the virtual coordinates has few or no dead ends even in critical sparse networks

      • virtual distance reflects real distance

      • dv ≈ c · dh , c is a constant


What s hop id

L3

416

650

541

414

416

432

652

305

L2

323

543

A

224

215

654

125

L1

335

036

What’s Hop ID

  • Hop distances of a node to all the landmarks are combined into a vector, i.e. the node’s Hop ID.


Lower and upper bounds
Lower and Upper Bounds

B

Triangulation inequality

  • Hop ID of A is

  • Hop ID of B is

(1)

(2)

A

Li


Lower bound better than upper bound
Lower Bound Better Than Upper Bound

  • One example: 3200 nodes, density λ=3π

  • Lower bound is much closer to hop distance


Lower bound still not the best
Lower Bound Still Not The Best

650

  • H(S) = 2 1 5

  • H(A) = 2 2 4

  • H(D) = 5 4 3

  • L(S, D) = L(A, D) = 3

  • |H(S) – H(D)|= 3 3 2

  • |H(A) – H(D)|=3 2 1

416

L3

541

414

416

432

652

305

L2

323

543

S

A

D

224

215

654

125

335

L1

036


Other distance functions
Other Distance Functions

  • Make use of the whole Hop ID vector

  • If p = ∞,

  • If p = 1,

  • If p = 2,

  • What values of p should be used?


The practical distance function
The Practical Distance Function

  • The distance function d should be able to reflect the hop distance dh

    • d ≈ c ·dh, c is a constant

    • L is quite close to dh (c = 1)

  • If p = 1 or 2, Dp deviates from L severely and arbitrarily

  • When p is large, Dp≈ L ≈ dh

    • p = 10, as we choose in simulations


Power distance better than lower bound
Power Distance Better Than Lower Bound

  • 3200 nodes, density λ=3π


Outline2
Outline

  • Motivation

  • Hop ID and Distance Function

  • Dealing with Dead Ends

  • Evaluation

  • Conclusion


Dealing with dead end problem
Dealing with Dead End Problem

  • With accurate distance function based on Hop ID, dead ends are less, but still exist

  • Landmark-guided algorithm to mitigate dead end problem

    • Send packet to the closest landmark to the destination

    • Limit the hops in this detour mode

  • Expending ring as the last solution


Example of landmark guided algorithm
Example of Landmark Guided Algorithm

Dp(S, D)>Dp(A, D)

Greedy Mode

Detour Mode

650

416

L3

541

414

S

P

432

A

652

416

305

L2

323

D

543

Dp(S, D)<Dp(L2,D)

Dead End

224

215

654

125

335

L1

036


Practical issues
Practical Issues

  • Landmark selection and maintenance

    • O(m·N) where m is the number of landmarks and N is the number of nodes

  • Hop ID adjustment

    • Mobile scenarios

    • Integrate Hop ID adjustment process into HELLO message (no extra overhead)

  • Location server

    • Can work with existing LSes such as CARD, or

    • Landmarks act as location servers


Outline3
Outline

  • Motivation

  • Hop ID and Distance Function

  • Dealing with Dead Ends

  • Evaluation

  • Conclusion


Evaluation methodology
Evaluation Methodology

  • Simulation model

    • Ns2, not scalable

    • A scalable packet level simulator

      • No MAC details

      • Scale to 51,200 nodes

  • Baseline experiment design

    • N nodes distribute randomly in a 2D square

    • Unit disk model: identical transmission range

  • Evaluation metrics

    • Routing success ratio

    • Shortest path stretch

    • Flooding range


Evaluation scenarios
Evaluation Scenarios

  • Landmark sensitivity

  • Density

  • Scalability

  • Mobility

  • Losses

  • Obstacles

  • 3-D space

  • Irregular shape and voids


Simulated protocols
Simulated Protocols

  • HIR-G: Greedy only

  • HIR-D: Greedy + Detour

  • HIR-E: Greedy + Detour + Expending ring

  • GFR: Greedy geographic routing

  • GWL: Geographic routing without location information [Mobicom03]

  • GOAFR+: Greedy Other Adaptive Face Routing [Mobihoc03]


Number of landmarks
Number of Landmarks

  • 3200 nodes, density shows average number of neighbors

  • Performance improves slowly after certain value (20)

  • Select 30 landmarks in simulations


Density
Density

  • HIR-D keeps high routing success ratio even in the scenarios with critical sparse density.

  • Shortest path stretch of HIR-G & HIR-D is close to 1.


Scalability
Scalability

  • HIR-D degrades slowly as network becomes larger

  • HIR-D is not sensitive to number of landmarks


Conclusions
Conclusions

  • Hop ID distance accurately reflects the hop distance and

  • Hop ID base routing performs very well in sparse networks and solves the dead end problem

  • Overhead of building and maintaining Hop ID coordinates is low


Secure wireless communication
Secure Wireless Communication

  • Secure communication in high-speed WiMAX networks

    • Design secure communication protocols through formal methods and vulnerability analysis

    • Wireless network anomaly/intrusion detection

      • Separating noises, interference, hidden terminal problems, etc.


Future work sensor networks 1
Future Work: Sensor Networks (1)

  • Topology Control in Sensor Networks

    • Motivation

      • Optimize sensing coverage and communication coverage

    • Sensing coverage

      • Active nodes cover all the required area without holes

        • Let as many as possible nodes to sleep to save energy

    • Communication coverage

      • Select active nodes to form a well-connected network

        • Enable simple routing

        • Routing paths are good in terms of bandwidth, delay and energy cost


Future work sensor networks 2
Future Work: Sensor Networks (2)

  • Routing in Sensor Networks

    • Motivation

      • Optimize lifetime of sensors

      • Avoid hotspots

    • Proposed routing: Position-based routing

      • Distance metric takes energy cost into account, e.g., HopID


Future work delay tolerant networks
Future Work: Delay Tolerant Networks

  • Applications

    • Interplanetary Internet

    • Spacecraft communications

    • Mobile ad hoc networks w/ disconnections (Zebranet)

    • Military/tactical networks

    • Disaster response

  • Challenges

    • Stochastic Mobility

    • Sparse connectivity

      • May not have contemporaneous end-to-end path

    • Delay tolerability

      • With an upper bound of the delay (e.g., Mars: 40 min RTT)

    • Limited buffer size

  • Focus: Routing and Message Delivery


Research methodology
Research methodology

Combination of theory, synthetic/real trace driven simulation, and real-world implementation and deployment


Thank you
Thank You!

Questions?


Related work to dead end problem
Related Work to Dead End Problem

  • Fix dead end problem

    • Improves face routing: GPSR, GOAFR+, GPVFR

    • Much longer routing path than shortest path

  • Reduce dead ends

    “Geographic routing without location information” [Rao et al, mobicom03]

    • Works well in dense networks

    • Outperforms geographic coordinates if obstacles or voids exist

    • Virtual coordinates are promising in reducing dead ends

    • However, degrades fast as network becomes sparser


How tight are the bounds
How Tight Are The Bounds?

  • Theorem [FOCS'04)]

    • Given a certain number (m) of landmarks, with high probability, for most nodes pairs, L and U can give a tight bound of hop distance

      • m doesn’t depend on N, number of nodes

    • Example: If there are m landmarks, with high probability, for 90% of node pairs, we have U≤1.1L


U is not suitable for routing
U Is Not Suitable for Routing

  • If two nodes are very close and no landmarks are close to these two nodes or the shortest path between the two nodes, U is prone to be an inaccurate estimation

  • U(A, B) = 5, while dh(A, B)=2

L3

416

650

541

414

416

432

652

305

L2

B

323

543

A

224

215

654

125

L1

335

036


Landmark selection
Landmark Selection

9

0

13

15

11

8

8

10

12

4

4


Hop id adjustment
Hop ID Adjustment

  • Mobility changes topology

  • Reflooding costs too much overhead

  • Adopt the idea of distance vector

03

13

12

Neighbors

{23, 13}

Neighbors

{23}

32

21

34

24

23

30


Build hop id system
Build Hop ID System

  • Build a shortest path tree

  • Aggregate landmark candidates

  • Inform landmarks

  • Build Hop ID

    • Landmarks flood to the whole network.

  • Overall cost

    • O(m*n), m = number of LMs, n=number of nodes



Motivation

9

worse

8

7

Connectivity

6

5

Shortest Path Span

4

3

Greedy success

better

2

1

Motivation

1

  • Geographic routing suffers from dead end problem in sparse networks

0.9

0.8

GFG/GPSR

0.7

0.6

Frequency

0.5

0.4

GOAFR+

0.3

0.2

0.1

critical

0

4

6

8

10

12

Network Density [nodes per unit disk]

  • Fabian Kun, Roger Wattenhofer and Aaron Zollinger, Mobihoc 2003


Virtual coordinates1
Virtual Coordinates

  • Problem definition

    • Define the virtual coordinates

      • Select landmarks

      • Nodes measure the distance to landmarks

      • Nodes obtain virtual coordinates

    • Define the distance function

    • Goal: virtual distance reflects real distance

    • dv ≈ c · dh , c is a constant